Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2020

Abstract

Framing is an indispensable narrative device for news media because even the same facts may lead to conflicting understandings if deliberate framing is employed. Therefore, identifying media framing is a crucial step to understanding how news media influence the public. Framing is, however, difficult to operationalize and detect, and thus traditional media framing studies had to rely on manual annotation, which is challenging to scale up to massive news datasets. Here, by developing a media frame classifier that achieves state-of-the-art performance, we systematically analyze the media frames of 1.5 million New York Times articles published from 2000 to 2017. By examining the ebb and flow of media frames over almost two decades, we show that short-term frame abundance fluctuation closely corresponds to major events, while there also exist several long-term trends, such as the gradually increasing prevalence of the “Cultural identity” frame. By examining specific topics and sentiments, we identify characteristics and dynamics of each frame. Finally, as a case study, we delve into the framing of mass shootings, revealing three major framing patterns. Our scalable, computational approach to massive news datasets opens up new pathways for systematic media framing studies.

Keywords

Computational journalism, Media frames corpus, Media framing

Discipline

Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

WebSci '20: 12th ACM Conference on Web Science, Southampton, United Kingdom, July 6-10

Editor

KWAK, Haewoon; AN, Jisun; AHN, Yong-Yeol.

First Page

305

Last Page

314

ISBN

9781450379892

Identifier

10.1145/3394231.3397921

Publisher

Association for Computing Machinery, Inc

City or Country

New York

Additional URL

https://doi,org/10.1145/3394231.3397921

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